1. Field
The present disclosure relates generally to machine learning and, more particularly, to machine learning of situations via pattern matching or recognition for use in or with mobile communication devices.
2. Information
Mobile communication devices, such as, for example, cellular telephones, smart telephones, portable navigation units, laptop computers, personal digital assistants, or the like are becoming more common every day. These devices may include, for example, a variety of sensors to support a number of host applications. Typically, although not necessarily, sensors are capable of converting physical phenomena into analog or digital signals and may be integrated into (e.g., built-in, etc.) or otherwise supported by (e.g., stand-alone, etc.) a mobile communication device. For example, a mobile communication device may feature one or more accelerometers, gyroscopes, magnetometers, gravitometers, ambient light detectors, proximity sensors, thermometers, location sensors, microphones, cameras, etc., capable of measuring various motion states, locations, positions, orientations, ambient environments, etc. of the mobile device. Sensors may be utilized individually or may be used in combination with other sensors, depending on an application.
A popular and rapidly growing market trend in sensor-enabled technology includes, for example, intelligent or smart mobile communication devices that may be capable of understanding what associated users are doing (e.g., user activities, intentions, goals, etc.) so as to assist, participate, or, at times, intervene in a more meaningful way. Integration of an ever-expanding variety or suite of embedded or associated sensors that continually capture, obtain, or process large volumes of incoming information streams may, however, present a number of challenges. These challenges may include, for example, multi-sensor parameter tracking, multi-modal information stream integration, increased signal pattern classification or recognition complexity, background processing bandwidth requirements, or the like, which may be at least partially attributed to a more dynamic environment created by user mobility. Accordingly, how to capture, integrate, or otherwise process multi-dimensional sensor information in an effective or efficient manner for a more satisfying user experience continues to be an area of development.